Optimizing Product Provenance Verification using Data Valuation Methods

TL;DR

This paper presents a data valuation framework using Shapley values to improve the selection of training data in isotope-based geographic origin verification, enhancing model robustness and accuracy in supply chain monitoring.

Contribution

It introduces a novel data valuation method that guides strategic sampling in isotope analysis models, deployed in a real-world provenance verification system.

Findings

Improved model robustness and accuracy in provenance verification.

Effective prioritization of high-informative samples for training.

Enhanced regulatory enforcement and fraud mitigation.

Abstract

Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives for misrepresentation of commodities, such as hiding the origin of illegally harvested timber or stolen agricultural products. Stable Isotope Ratio Analysis (SIRA), combined with Gaussian process regression-based isoscapes, has emerged as a powerful tool for geographic origin verification. While these models are now actively deployed in operational settings supporting regulators, certification bodies, and companies, they remain constrained by data scarcity and suboptimal dataset selection. In this work, we introduce a novel deployed data valuation framework designed to enhance the selection and utilization of training data for machine learning models applied in SIRA. By quantifying the marginal utility of individual samples using Shapley values, our method guides strategic, cost-effective, and robust sampling campaigns within active monitoring programs. By prioritizing high-informative samples, our approach improves model robustness and predictive accuracy across diverse datasets and geographies. Our framework has been implemented and validated in a live provenance verification system currently used by enforcement agencies, demonstrating tangible, real-world impact. Through extensive experiments and deployment in a live provenance verification system, we show that this system significantly enhances provenance verification, mitigates fraudulent trade practices, and strengthens regulatory enforcement of global supply chains.